Return a value from a Python function
Compute with numbers
Normalize text
Select a branch
Combine conditions
Repeat work with a loop
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Write Python code that prepares data, validates AI application payloads, and retrieves relevant text using deterministic exercises with NumPy and pandas.
AI applications depend on ordinary data work: reading records, checking their shape, preparing numeric inputs, and deciding which results belong in a response. Hands-On: Python for AI gives you practice with that work through 48 Python coding exercises, starting with the language itself and building toward two integrated workflows. Every task uses supplied data and deterministic results, so you can concentrate on the code and its behavior.
You begin with expressions, strings, branches, and loops, then work with lists, dictionaries, and nested records. Functions, imports, validation, error handling, and type hints help you organize those transformations. The data chapters cover UTF-8 text, CSV, JSON, and JSONL before introducing NumPy arrays and pandas tables. You practice shape checks, filtering, reductions, missing values, grouping, and joins while returning clear, ordinary Python values.
The later exercises use message payloads, structured response data, and bounded batches. You then learn the mathematics needed to compare fixed embeddings, rank tied scores consistently, select relevant records, and assemble text within a context budget. The final workflow combines those decisions into a retrieval request. This course suits programmers who know another language and want a practical route into Python for AI application data. You do not need prior Python experience or a model service account.
